Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data

Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (I...
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Autor*in:	Jaeger-Honz, Sabrina [verfasserIn] Klein, Karsten Schreiber, Falk

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Interaction fingerprints Molecular dynamics simulation Microcystin Aggregation Visualisation

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Journal of cheminformatics - Springer International Publishing, 2009, 16(2024), 1 vom: 12. März
Übergeordnetes Werk:	volume:16 ; year:2024 ; number:1 ; day:12 ; month:03

Links:	Volltext

DOI / URN:	10.1186/s13321-024-00822-3

Katalog-ID:	SPR055121853

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520			\|a Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets.
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allfields	10.1186/s13321-024-00822-3 doi (DE-627)SPR055121853 (SPR)s13321-024-00822-3-e DE-627 ger DE-627 rakwb eng 540 VZ Jaeger-Honz, Sabrina verfasserin aut Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. Interaction fingerprints (dpeaa)DE-He213 Molecular dynamics simulation (dpeaa)DE-He213 Microcystin (dpeaa)DE-He213 Aggregation (dpeaa)DE-He213 Visualisation (dpeaa)DE-He213 Klein, Karsten aut Schreiber, Falk aut Enthalten in Journal of cheminformatics Springer International Publishing, 2009 16(2024), 1 vom: 12. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:16 year:2024 number:1 day:12 month:03 https://dx.doi.org/10.1186/s13321-024-00822-3 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 12 03
spelling	10.1186/s13321-024-00822-3 doi (DE-627)SPR055121853 (SPR)s13321-024-00822-3-e DE-627 ger DE-627 rakwb eng 540 VZ Jaeger-Honz, Sabrina verfasserin aut Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. Interaction fingerprints (dpeaa)DE-He213 Molecular dynamics simulation (dpeaa)DE-He213 Microcystin (dpeaa)DE-He213 Aggregation (dpeaa)DE-He213 Visualisation (dpeaa)DE-He213 Klein, Karsten aut Schreiber, Falk aut Enthalten in Journal of cheminformatics Springer International Publishing, 2009 16(2024), 1 vom: 12. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:16 year:2024 number:1 day:12 month:03 https://dx.doi.org/10.1186/s13321-024-00822-3 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 12 03
allfields_unstemmed	10.1186/s13321-024-00822-3 doi (DE-627)SPR055121853 (SPR)s13321-024-00822-3-e DE-627 ger DE-627 rakwb eng 540 VZ Jaeger-Honz, Sabrina verfasserin aut Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. Interaction fingerprints (dpeaa)DE-He213 Molecular dynamics simulation (dpeaa)DE-He213 Microcystin (dpeaa)DE-He213 Aggregation (dpeaa)DE-He213 Visualisation (dpeaa)DE-He213 Klein, Karsten aut Schreiber, Falk aut Enthalten in Journal of cheminformatics Springer International Publishing, 2009 16(2024), 1 vom: 12. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:16 year:2024 number:1 day:12 month:03 https://dx.doi.org/10.1186/s13321-024-00822-3 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 12 03
allfieldsGer	10.1186/s13321-024-00822-3 doi (DE-627)SPR055121853 (SPR)s13321-024-00822-3-e DE-627 ger DE-627 rakwb eng 540 VZ Jaeger-Honz, Sabrina verfasserin aut Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. Interaction fingerprints (dpeaa)DE-He213 Molecular dynamics simulation (dpeaa)DE-He213 Microcystin (dpeaa)DE-He213 Aggregation (dpeaa)DE-He213 Visualisation (dpeaa)DE-He213 Klein, Karsten aut Schreiber, Falk aut Enthalten in Journal of cheminformatics Springer International Publishing, 2009 16(2024), 1 vom: 12. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:16 year:2024 number:1 day:12 month:03 https://dx.doi.org/10.1186/s13321-024-00822-3 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 12 03
allfieldsSound	10.1186/s13321-024-00822-3 doi (DE-627)SPR055121853 (SPR)s13321-024-00822-3-e DE-627 ger DE-627 rakwb eng 540 VZ Jaeger-Honz, Sabrina verfasserin aut Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. Interaction fingerprints (dpeaa)DE-He213 Molecular dynamics simulation (dpeaa)DE-He213 Microcystin (dpeaa)DE-He213 Aggregation (dpeaa)DE-He213 Visualisation (dpeaa)DE-He213 Klein, Karsten aut Schreiber, Falk aut Enthalten in Journal of cheminformatics Springer International Publishing, 2009 16(2024), 1 vom: 12. März (DE-627)594779219 (DE-600)2486539-4 1758-2946 nnns volume:16 year:2024 number:1 day:12 month:03 https://dx.doi.org/10.1186/s13321-024-00822-3 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2024 1 12 03
language	English
source	Enthalten in Journal of cheminformatics 16(2024), 1 vom: 12. März volume:16 year:2024 number:1 day:12 month:03
sourceStr	Enthalten in Journal of cheminformatics 16(2024), 1 vom: 12. März volume:16 year:2024 number:1 day:12 month:03
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Interaction fingerprints Molecular dynamics simulation Microcystin Aggregation Visualisation
dewey-raw	540
isfreeaccess_bool	true
container_title	Journal of cheminformatics
authorswithroles_txt_mv	Jaeger-Honz, Sabrina @@aut@@ Klein, Karsten @@aut@@ Schreiber, Falk @@aut@@
publishDateDaySort_date	2024-03-12T00:00:00Z
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author	Jaeger-Honz, Sabrina
spellingShingle	Jaeger-Honz, Sabrina ddc 540 misc Interaction fingerprints misc Molecular dynamics simulation misc Microcystin misc Aggregation misc Visualisation Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data
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topic_title	540 VZ Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data Interaction fingerprints (dpeaa)DE-He213 Molecular dynamics simulation (dpeaa)DE-He213 Microcystin (dpeaa)DE-He213 Aggregation (dpeaa)DE-He213 Visualisation (dpeaa)DE-He213
topic	ddc 540 misc Interaction fingerprints misc Molecular dynamics simulation misc Microcystin misc Aggregation misc Visualisation
topic_unstemmed	ddc 540 misc Interaction fingerprints misc Molecular dynamics simulation misc Microcystin misc Aggregation misc Visualisation
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title	Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data
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title_full	Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data
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title_sort	systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data
title_auth	Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data
abstract	Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. © The Author(s) 2024
abstractGer	Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. © The Author(s) 2024
abstract_unstemmed	Abstract Computational methods such as molecular docking or molecular dynamics (MD) simulations have been developed to simulate and explore the interactions between biomolecules. However, the interactions obtained using these methods are difficult to analyse and evaluate. Interaction fingerprints (IFPs) have been proposed to derive interactions from static 3D coordinates and transform them into 1D bit vectors. More recently, the concept has been applied to derive IFPs from MD simulations, which adds a layer of complexity by adding the temporal motion and dynamics of a system. As a result, many IFPs are obtained from one MD simulation, resulting in a large number of individual IFPs that are difficult to analyse compared to IFPs derived from static 3D structures. Scientific contribution: We introduce a new method to systematically aggregate IFPs derived from MD simulation data. In addition, we propose visualisations to effectively analyse and compare IFPs derived from MD simulation data to account for the temporal evolution of interactions and to compare IFPs across different MD simulations. This has been implemented as a freely available Python library and can therefore be easily adopted by other researchers and to different MD simulation datasets. © The Author(s) 2024
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title_short	Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data
url	https://dx.doi.org/10.1186/s13321-024-00822-3
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up_date	2024-07-03T13:31:05.479Z
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Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data

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